
import pandas as pd
import numpy as np
url = "https://docs.google.com/spreadsheets/d/1mS6khEB6m8cPNenNvY9Tg6bJ6YkmcvCI/export?format=csv&gid=1283335856"
df = pd.read_csv(url)
df.head()


important_columns = ["SUBJECT", "NBR", "COURSE NAME", "PROF", "TOTAL", "A+", "A", "A-", "B+", "B", "B-", "C+", "C", "C-", "D", "F", "W", "AVG GPA"]

def gpa_letter_converter(gpa):
    letter_grades = {
        "A": 4.0, 
        "A-": (3.7, 3.8, 3.9), 
        "B+": (3.3, 3.4, 3.5, 3.6), 
        "B": (3.0, 3.1, 3.2), 
        "B-": (2.7, 2.8, 2.9), 
        "C+": (2.3, 2.4, 2.5, 2.6),
        "C": (2.0, 2.1, 2.2), 
        "C-": (1.7, 1.8, 1.9),
        "D": (1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6),
        "F": (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)
    }
    for letter_grade, number_grade in letter_grades.items():
        if isinstance(number_grade, float) and gpa == number_grade:
            return letter_grade
        elif isinstance(number_grade, tuple) and gpa in number_grade:
            return letter_grade
    
    return None

analysis_df = df[important_columns]
analysis_df.head()


analysis_df = analysis_df.dropna(subset = "PROF")
analysis_df = analysis_df[analysis_df["AVG GPA"] != 0]
analysis_df.shape

(2333, 18)


math_df = analysis_df[analysis_df['SUBJECT']== 'MATH']
pd.set_option('display.max_rows', None)
math_df


CourseNumberPattern = r'^[1-3][0-9]{2}W?$'
math_df = math_df[math_df['NBR'].str.contains(CourseNumberPattern)]
math_df


def calculate_average_gpas(df):
    # Prepare a list to store the results
    results = []

    # GPA equivalents for each letter grade
    letter_grades_to_gpa = {
        "A+": 4.0,
        "A": 4.0,
        "A-": 3.7,
        "B+": 3.3,
        "B": 3.0,
        "B-": 2.7,
        "C+": 2.3,
        "C": 2.0,
        "C-": 1.7,
        "D": 1.0,
        "F": 0.0
    }

    # Loop over all unique course numbers
    for class_nbr in df["NBR"].unique():
        # Filter the DataFrame for the current course number
        df_nbr = df[df["NBR"] == class_nbr]

        # Loop over all unique professors for the current course number
        for prof in df_nbr["PROF"].unique():
            # Filter the DataFrame for the current professor
            df_prof = df_nbr[df_nbr["PROF"] == prof]

            # Calculate the average GPA for the current professor and course number
            avg_gpa_prof = round(df_prof["AVG GPA"].mean(), 1)

            # Convert the individual grade counts to GPA equivalents and calculate the standard deviation
            gpa_distributions = []
            for grade_letter, gpa in letter_grades_to_gpa.items():
                gpa_distributions.extend([gpa] * df_prof[grade_letter].sum())
            std_dev_gpa_prof = round(np.std(gpa_distributions), 2)

            # Append the result to the list
            results.append({
                "CLASS NUMBER": class_nbr,
                "PROF": prof,
                "AVG GPA PROF": avg_gpa_prof,
                "AVG GPA PROF LETTER": gpa_letter_converter(avg_gpa_prof),
                "STD DEV GPA PROF": std_dev_gpa_prof
            })

        # If there is more than one professor for this course, calculate the average GPA for the current course number, regardless of the professor
        if df_nbr["PROF"].nunique() > 1:
            avg_gpa_nbr = round(df_nbr["AVG GPA"].mean(), 1)

            # Convert the individual grade counts to GPA equivalents and calculate the standard deviation
            gpa_distributions = []
            for grade_letter, gpa in letter_grades_to_gpa.items():
                gpa_distributions.extend([gpa] * df_nbr[grade_letter].sum())
            std_dev_gpa_nbr = round(np.std(gpa_distributions), 2)

            # Append the result to the list
            results.append({
                "CLASS NUMBER": class_nbr,
                "PROF": "All Professors",
                "AVG GPA PROF": avg_gpa_nbr,
                "AVG GPA PROF LETTER": gpa_letter_converter(avg_gpa_nbr),
                "STD DEV GPA PROF": std_dev_gpa_nbr
            })

    # Convert the list of results to a DataFrame
    df_results = pd.DataFrame(results)
    
    # Find the hardest class based on average GPA
    hardest_class = df_results[df_results["PROF"] == "All Professors"].sort_values("AVG GPA PROF").iloc[0]

    # Calculate the average GPA for the entire subject
    avg_gpa_subject = round(df["AVG GPA"].mean(), 1)

    print(f"Average GPA for this entire subject in Fall 2022 is: {avg_gpa_subject}, which is a {gpa_letter_converter(avg_gpa_subject)}")
    print(f"The hardest class based on average GPA in Fall 2022 is class number: {hardest_class['CLASS NUMBER']} with an average GPA of {hardest_class['AVG GPA PROF']}, which is a {hardest_class['AVG GPA PROF LETTER']}")
    print("\nStandard deviation tells us about the spread of the grades that students received in each class. A higher standard deviation indicates a wider range of grades, while a lower standard deviation indicates that grades were more closely clustered around the average.")

    return df_results


math_df_results = calculate_average_gpas(math_df)
math_df_results

Average GPA for this entire subject in Fall 2022 is: 2.3, which is a C+
The hardest class based on average GPA in Fall 2022 is class number: 151 with an average GPA of 1.9, which is a C-

Standard deviation tells us about the spread of the grades that students received in each class. A higher standard deviation indicates a wider range of grades, while a lower standard deviation indicates that grades were more closely clustered around the average.


import matplotlib.pyplot as plt
import seaborn as sns


plt.figure()
all_prof_df_results = math_df_results[math_df_results["PROF"] == 'All Professors']
sns.histplot(all_prof_df_results["STD DEV GPA PROF"], kde=True, color='skyblue')
plt.axvline(all_prof_df_results["STD DEV GPA PROF"].mean(), color='red', linestyle='dashed', linewidth=1, label='Mean')
min_ylim, max_ylim = plt.ylim()
plt.text(all_prof_df_results["STD DEV GPA PROF"].mean()*1.2, max_ylim*0.9, 'Mean: {:.2f}'.format(all_prof_df_results["STD DEV GPA PROF"].mean()))

# Generate a color palette with as many colors as classes
colors = sns.color_palette("husl", len(all_prof_df_results))

for idx, (_, row) in enumerate(all_prof_df_results.iterrows()):
    plt.axvline(row["STD DEV GPA PROF"], color=colors[idx], linestyle='dotted', linewidth=0.5, label=row["CLASS NUMBER"])

plt.title("Distribution of Standard Deviations of GPAs")
plt.xlabel("Standard Deviation")
plt.ylabel("Frequency")
plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1))  # Adjusted the location to ensure it doesn't overlap with the plot
plt.show()


def calculate_teacher_gpas(df):
    # Prepare a list to store the results
    results = []

    # GPA equivalents for each letter grade
    letter_grades_to_gpa = {
        "A+": 4.0,
        "A": 4.0,
        "A-": 3.7,
        "B+": 3.3,
        "B": 3.0,
        "B-": 2.7,
        "C+": 2.3,
        "C": 2.0,
        "C-": 1.7,
        "D": 1.0,
        "F": 0.0
    }

    # Loop over all unique professors
    for prof in sorted(df["PROF"].unique()):
        # Filter the DataFrame for the current professor
        df_prof = df[df["PROF"] == prof]

        # Calculate the average GPA for the current professor
        avg_gpa_prof = round(df_prof["AVG GPA"].mean(), 1)

        # Convert the individual grade counts to GPA equivalents and calculate the standard deviation
        gpa_distributions = []
        for grade_letter, gpa in letter_grades_to_gpa.items():
            gpa_distributions.extend([gpa] * df_prof[grade_letter].sum())
        std_dev_gpa_prof = round(np.std(gpa_distributions), 1)

        # Calculate the percentage of students who withdrew for the current professor
        withdraw_percentage = df_prof["W"].sum() / df_prof["TOTAL"].sum()

        # Append the result to the list
        results.append({
            "PROF": prof,
            "AVG GPA PROF": avg_gpa_prof,
            "AVG GPA PROF LETTER": gpa_letter_converter(avg_gpa_prof),
            "STD DEV GPA PROF": std_dev_gpa_prof,
            "NUM OF CLASSES": len(df_prof),
            "WITHDRAW PERCENTAGE": round(withdraw_percentage * 100, 1)
        })

    # Convert the list of results to a DataFrame
    df_results = pd.DataFrame(results)

    # Calculate the average GPA for the entire subject
    avg_gpa_subject = round(df["AVG GPA"].mean(), 1)

    # Calculate the average standard deviation for the entire subject
    gpa_distributions = []
    for grade_letter, gpa in letter_grades_to_gpa.items():
        gpa_distributions.extend([gpa] * df[grade_letter].sum())
    avg_std_dev_subject = round(np.std(gpa_distributions), 1)
    
    # Calculate the average withdrawal percentage for the entire subject
    withdraw_percentage_subject = round((df["W"].sum() / df["TOTAL"].sum()) * 100, 2)
    
    # Find the professors who teach more than one class, have a GPA that is the same or higher than the subject average GPA,
    # and for whom less than 40% of students withdrew
    best_profs = df_results[(df_results["NUM OF CLASSES"] > 1) & (df_results["AVG GPA PROF"] >= avg_gpa_subject) & (df_results["WITHDRAW PERCENTAGE"] <= withdraw_percentage_subject)]

    print(f"Professors who teach more than one class, have a GPA that is the same or higher than the subject average GPA ({avg_gpa_subject}), and have a withdrawal percentage that is less or equal than the subject average withdrawal percentage ({withdraw_percentage_subject}%):\n")
    print(best_profs, "\n")
    print("Note: This ignores rate my professor ratings. This is soley based on GPA and it doesn't consider how much students actually learn from these teachers!")
    print(f"Average standard deviation of GPA for all teachers in this subject in Fall 2022 is: {avg_std_dev_subject}")

    return df_results


teacher_math_df_results = calculate_teacher_gpas(math_df)
teacher_math_df_results

Professors who teach more than one class, have a GPA that is the same or higher than the subject average GPA (2.3), and have a withdrawal percentage that is less or equal than the subject average withdrawal percentage (24.98%):

           PROF  AVG GPA PROF AVG GPA PROF LETTER  STD DEV GPA PROF  \
1        ALI, M           3.4                  B+               0.8   
8     BROGES, A           2.6                  C+               1.5   
9        CAI, A           3.5                  B+               0.6   
12    CLEARY, J           3.0                   B               1.1   
17     FLYNN, D           2.6                  C+               1.2   
22   GOLDMAN, S           2.9                  B-               1.0   
24  GREENMAN, J           2.4                  C+               1.3   
26     ISLAM, M           2.3                  C+               1.5   
33    KLOSIN, K           2.8                  B-               1.2   
37       LEE, H           3.1                   B               0.8   
38    LEHMAN, S           2.3                  C+               1.4   
47  NAKAYAMA, A           2.5                  C+               1.3   
55  RICCARDO, M           2.8                  B-               1.2   
59     SARIC, D           3.0                   B               0.9   
61    SISSER, F           2.4                  C+               1.3   
69      WANG, A           2.5                  C+               1.2   
70       WEN, C           2.5                  C+               1.5   
74    ZAKERI, S           3.0                   B               0.9   

    NUM OF CLASSES  WITHDRAW PERCENTAGE  
1                3                 12.6  
8                3                 13.3  
9                2                  5.8  
12               2                 11.0  
17               2                 10.0  
22               3                  6.5  
24               4                 21.1  
26               2                 22.6  
33               2                 12.0  
37               2                 23.3  
38               3                 17.5  
47               2                 18.2  
55               2                 13.3  
59               2                 23.3  
61               3                 18.5  
69               2                 24.6  
70               2                  7.4  
74               2                 15.0   

Note: This ignores rate my professor ratings. This is soley based on GPA and it doesn't consider how much students actually learn from these teachers!
Average standard deviation of GPA for all teachers in this subject in Fall 2022 is: 1.4


import plotly.express as px

# Set the overall average GPA
mean_gpa = 2.3

# Create the scatter plot using Plotly Express
fig = px.scatter(teacher_math_df_results, 
                 x=list(range(len(teacher_math_df_results))),
                 y='AVG GPA PROF',
                 hover_name='PROF', # This will show the professor's name when hovering over a point
                 title="MATH Professor GPA Averages vs MATH Subject Average GPA",
                 labels={'x': 'Professor (By Index Above)', 'y': 'Average GPA'},
                 size_max=100)

# Add a line for the average GPA
fig.add_shape(
    type='line',
    line=dict(dash='dash', color='red'),
    x0=0,
    x1=len(teacher_math_df_results),
    y0=mean_gpa,
    y1=mean_gpa,
)

# Show the plot
fig.show()


# Count the number of classes with an average GPA at or greater than 3.0 and those less than 3.0
green_percentage = teacher_math_df_results[teacher_math_df_results['AVG GPA PROF'] >= 3.0].shape[0]
red_percentage = teacher_math_df_results[teacher_math_df_results['AVG GPA PROF'] < 3.0].shape[0]

# Create the values and labels for the pie chart
values = [green_percentage, red_percentage]
labels = ['At or above 3.0 (B or above)', 'At or below 2.7 (B- or below)']

# Define the colors for each section (green and red)
colors = ['#77dd77', '#ff6961']

# Plot the pie chart
plt.figure(figsize = (6, 6))
plt.pie(values, labels = labels, colors = colors, autopct = '%1.1f%%')

# Set the title
plt.title("Average GPA of all MATH Teachers")

# Show the plot
plt.show()

	TERM	SUBJECT	NBR	COURSE NAME	SECTION	PROF	TOTAL	A+	A	A-	...	C	C-	D	F	W	INC/NA	AVG GPA	join the best club on campus	Unnamed: 22	https://discord.gg/vxCqd4hdak
0	F2022	ACCT	100	Fin & Mgr Acct	1	HO, V	28	0	11	2	...	3	0	0	0	5	0	3.383	NaN	NaN	NaN
1	F2022	ACCT	101	Intro Thry & Prac of Acct I	11	CHAN, J	29	0	13	2	...	0	1	0	0	3	0	3.464	NaN	NaN	NaN
2	F2022	ACCT	101	Intro Thry & Prac of Acct I	5	RUTHIZER, S	50	2	9	14	...	1	0	3	2	3	0	3.176	NaN	NaN	NaN
3	F2022	ACCT	101	Intro Thry & Prac of Acct I	2	GRUZA, M	45	1	2	3	...	0	0	0	0	25	0	3.067	NaN	NaN	NaN
4	F2022	ACCT	101	Intro Thry & Prac of Acct I	4	FEISULLIN, A	45	4	6	1	...	9	1	1	0	4	1	2.908	NaN	NaN	NaN

	SUBJECT	NBR	COURSE NAME	PROF	TOTAL	A+	A	A-	B+	B	B-	C+	C	C-	D	F	W	AVG GPA
0	ACCT	100	Fin & Mgr Acct	HO, V	28	0	11	2	1	3	3	0	3	0	0	0	5	3.383
1	ACCT	101	Intro Thry & Prac of Acct I	CHAN, J	29	0	13	2	0	4	5	0	0	1	0	0	3	3.464
2	ACCT	101	Intro Thry & Prac of Acct I	RUTHIZER, S	50	2	9	14	6	4	5	0	1	0	3	2	3	3.176
3	ACCT	101	Intro Thry & Prac of Acct I	GRUZA, M	45	1	2	3	1	5	0	6	0	0	0	0	25	3.067
4	ACCT	101	Intro Thry & Prac of Acct I	FEISULLIN, A	45	4	6	1	7	5	0	6	9	1	1	0	4	2.908

	SUBJECT	NBR	COURSE NAME	PROF	TOTAL	A+	A	A-	B+	B	B-	C+	C	C-	D	F	W	AVG GPA
1757	MATH	110	Math Literacy	ALI, M	37	7	9	3	4	2	2	1	1	0	1	0	5	3.500
1758	MATH	110	Math Literacy	ALI, M	37	10	7	2	1	3	3	3	1	0	1	0	5	3.410
1759	MATH	110	Math Literacy	LASKER, M	31	3	8	2	3	0	4	0	1	0	0	2	7	3.222
1760	MATH	110	Math Literacy	BEGA, J	37	4	0	5	3	3	3	4	2	2	0	1	6	2.893
1761	MATH	110	Math Literacy	WOLF-SONKIN, V	30	0	1	1	0	6	1	0	2	2	4	5	7	1.809
1762	MATH	114	Elementary Prob&Stat	LEHMAN, S	20	1	1	4	0	5	0	2	0	1	3	0	3	2.771
1763	MATH	115	College Algebra for Precalc	ALI, M	29	6	5	1	4	3	2	0	1	2	1	0	3	3.268
1764	MATH	115	College Algebra for Precalc	UDDIN, J	30	0	3	1	3	1	2	0	1	3	0	1	14	2.740
1765	MATH	115	College Algebra for Precalc	CLARKE, A	22	1	0	1	1	2	1	0	1	0	0	1	11	2.713
1766	MATH	115	College Algebra for Precalc	TOBAR, J	30	1	2	3	1	4	0	1	5	2	3	0	2	2.595
1767	MATH	115	College Algebra for Precalc	NAKAYAMA, A	38	4	2	0	2	3	3	2	4	1	1	3	8	2.520
1768	MATH	115	College Algebra for Precalc	NAKAYAMA, A	39	3	1	3	7	0	2	4	2	1	5	3	6	2.435
1769	MATH	115	College Algebra for Precalc	CLARKE, A	21	0	0	1	1	0	5	0	1	1	0	1	6	2.420
1770	MATH	115	College Algebra for Precalc	OSTROWSKY, A	37	3	4	1	2	1	0	0	2	2	6	2	12	2.378
1771	MATH	115	College Algebra for Precalc	BOTEJU, W	38	0	3	2	0	2	3	2	2	4	2	5	8	2.036
1772	MATH	115	College Algebra for Precalc	LEE, D	35	0	3	0	4	2	0	1	0	6	9	1	9	2.027
1773	MATH	115	College Algebra for Precalc	GILLMAN, P	30	3	0	0	0	2	0	2	0	1	3	5	9	1.706
1774	MATH	115	College Algebra for Precalc	CLARKE, A	22	0	0	0	1	1	0	1	1	2	0	3	7	1.556
1775	MATH	115	College Algebra for Precalc	WOLF-SONKIN, V	30	0	0	0	1	0	3	1	1	2	1	4	16	1.546
1776	MATH	115	College Algebra for Precalc	LEE, D	35	0	0	0	2	0	2	0	1	8	7	3	9	1.504
1777	MATH	115	College Algebra for Precalc	LEHMAN, S	21	1	1	0	0	0	2	0	2	0	1	6	5	1.415
1778	MATH	115	College Algebra for Precalc	GILLMAN, P	36	0	2	0	0	1	0	2	1	2	2	8	12	1.278
1779	MATH	115	College Algebra for Precalc	NEVAREZ, B	30	0	0	0	2	0	2	2	1	1	3	8	9	1.226
1780	MATH	115	College Algebra for Precalc	CHOW, J	29	0	0	0	2	0	0	1	3	0	5	8	4	1.047
1781	MATH	115	College Algebra for Precalc	GONZALEZ, R	14	0	0	1	0	0	0	0	2	1	1	7	1	0.867
1782	MATH	119	Math for Elem School Teachers	LEE, H	25	4	2	3	3	3	4	2	0	0	1	0	3	3.200
1783	MATH	119	Math for Elem School Teachers	GREENMAN, J	30	3	5	2	1	4	0	2	8	1	0	0	4	2.962
1784	MATH	119	Math for Elem School Teachers	CHOW, J	29	5	5	1	3	1	2	2	2	2	0	2	1	2.960
1785	MATH	119	Math for Elem School Teachers	LIN, Y	30	6	4	1	5	1	2	0	3	1	3	2	2	2.832
1786	MATH	119	Math for Elem School Teachers	LEHMAN, S	16	2	1	2	1	1	1	0	1	0	3	1	2	2.569
1787	MATH	119	Math for Elem School Teachers	BERGER, K	36	2	4	5	3	1	2	0	1	2	9	1	5	2.507
1788	MATH	119	Math for Elem School Teachers	SHEN, T	36	1	2	1	2	2	4	3	2	4	5	2	7	2.207
1789	MATH	120	Discrete Math Comp Sci	GOLDMAN, S	35	0	9	6	2	3	2	9	0	1	2	0	1	3.047
1790	MATH	120	Discrete Math Comp Sci	WANG, A	35	4	2	1	2	0	3	5	2	3	2	1	10	2.600
1791	MATH	120	Discrete Math Comp Sci	JOANIDHI, Z	30	0	8	1	0	1	0	1	5	0	0	6	6	2.318
1792	MATH	120	Discrete Math Comp Sci	BERGER, K	35	0	1	0	2	6	3	2	2	2	4	2	11	2.196
1793	MATH	120	Discrete Math Comp Sci	BOTEJU, W	36	2	5	1	1	1	1	5	3	4	2	6	5	2.161
1794	MATH	120	Discrete Math Comp Sci	HANUSA, C	30	2	2	2	1	1	2	3	0	1	2	6	7	2.077
1795	MATH	120	Discrete Math Comp Sci	BOTEJU, W	35	1	1	2	2	4	2	0	2	1	5	6	8	1.927
1796	MATH	120	Discrete Math Comp Sci	BERGER, K	36	0	1	0	1	3	2	1	5	2	4	6	10	1.656
1797	MATH	120	Discrete Math Comp Sci	ADRIAN, M	37	0	2	0	1	1	3	0	4	3	7	8	8	1.466
1798	MATH	122	Precalculus	CAI, A	23	1	1	5	0	0	2	1	1	0	0	0	0	3.291
1799	MATH	122	Precalculus	CLEARY, J	38	6	11	3	1	2	0	1	2	1	5	0	4	3.169
1800	MATH	122	Precalculus	DHARMA, J	30	1	0	1	0	0	3	0	0	0	0	0	16	3.160
1801	MATH	122	Precalculus	LEE, H	35	5	1	2	4	3	4	2	0	3	0	0	11	3.088
1802	MATH	122	Precalculus	ARCHETTI, M	37	10	3	1	2	6	0	1	3	2	1	2	5	3.000
1803	MATH	122	Precalculus	CLEARY, J	35	3	3	4	2	2	6	2	3	0	4	0	4	2.834
1804	MATH	122	Precalculus	KALRA, P	20	3	1	0	0	0	4	3	2	0	0	1	6	2.693
1805	MATH	122	Precalculus	ISLAM, M	20	1	0	4	1	2	0	1	2	0	0	2	4	2.646
1806	MATH	122	Precalculus	OZKAN, E	10	0	3	0	0	0	0	3	0	0	2	0	2	2.613
1807	MATH	122	Precalculus	BROGES, A	21	0	4	1	0	6	1	1	1	0	1	3	1	2.539
1808	MATH	122	Precalculus	SPITZ, H	35	1	4	3	2	2	2	0	0	0	5	3	11	2.459
1809	MATH	122	Precalculus	LI, H	37	5	2	1	1	1	4	2	0	1	2	5	11	2.379
1810	MATH	122	Precalculus	LORD, A	37	0	4	0	4	1	1	2	4	0	4	2	9	2.341
1811	MATH	122	Precalculus	MARKOWITZ, E	40	0	3	4	3	4	3	1	3	2	4	4	8	2.339
1812	MATH	122	Precalculus	FLYNN, D	35	1	3	3	2	4	2	1	3	5	6	3	2	2.239
1813	MATH	122	Precalculus	COLON, C	37	2	1	0	0	1	0	2	1	3	4	0	19	2.193
1814	MATH	122	Precalculus	YUABOV, D	37	3	2	2	0	0	3	3	1	3	2	5	12	2.146
1815	MATH	122	Precalculus	MARKOWITZ, E	37	1	3	0	4	3	1	0	3	3	7	3	4	2.107
1816	MATH	122	Precalculus	JOSEPH, M	35	0	1	0	0	3	3	2	2	5	1	3	12	1.960
1817	MATH	122	Precalculus	SHEN, T	30	0	0	0	2	1	2	3	2	2	5	2	7	1.805
1818	MATH	122	Precalculus	JOSEPH, M	37	0	1	1	1	3	2	1	2	4	3	5	13	1.804
1819	MATH	122	Precalculus	GANT, S	35	0	2	2	0	1	0	0	2	1	4	4	14	1.756
1820	MATH	122	Precalculus	SPITZ, H	35	1	2	1	2	5	0	3	1	1	4	10	5	1.730
1821	MATH	122	Precalculus	TOLEDO, D	37	0	0	0	0	0	2	2	4	1	0	7	15	1.231
1822	MATH	122	Precalculus	TOLEDO, D	37	0	0	0	0	0	1	0	1	2	6	11	10	0.671
1823	MATH	131	Calculus with Applications I	GJONLEKAJ, M	16	2	2	2	0	2	0	2	1	0	0	1	4	3.000
1824	MATH	131	Calculus with Applications I	GREENMAN, J	29	1	7	0	0	2	1	1	4	1	3	0	9	2.785
1825	MATH	131	Calculus with Applications I	SABITOVA, M	20	0	2	0	1	4	0	1	4	2	0	1	5	2.467
1826	MATH	131	Calculus with Applications I	GREENMAN, J	24	1	1	0	1	2	2	0	4	2	2	3	5	2.006
1827	MATH	131	Calculus with Applications I	GREENMAN, J	31	0	4	0	2	3	1	3	2	0	3	7	6	1.928
1828	MATH	131	Calculus with Applications I	JOANIDHI, Z	25	0	2	0	1	3	1	1	4	0	0	6	6	1.850
1829	MATH	132	Calc Appl Soc Sci II	GOLDMAN, S	12	1	0	1	2	2	1	1	0	0	1	0	2	2.922
1830	MATH	141	Calculus/Differentiation	BROGES, A	19	5	3	1	0	1	1	0	0	0	1	1	3	3.262
1831	MATH	141	Calculus/Differentiation	KALRA, P	20	2	3	3	0	0	1	2	2	0	0	1	5	3.029
1832	MATH	141	Calculus/Differentiation	FLYNN, D	35	2	6	3	4	2	3	3	2	2	1	1	5	2.955
1833	MATH	141	Calculus/Differentiation	GOLDMAN, S	30	1	4	3	4	3	1	2	3	3	2	2	2	2.632
1834	MATH	141	Calculus/Differentiation	GONZALEZ, R	35	3	1	2	6	2	1	4	2	2	0	4	6	2.537
1835	MATH	141	Calculus/Differentiation	KIMATOV, M	38	7	3	2	0	2	2	2	1	3	4	5	5	2.403
1836	MATH	141	Calculus/Differentiation	WANG, A	34	3	1	1	5	2	4	2	2	0	2	5	7	2.356
1837	MATH	141	Calculus/Differentiation	NICASTRO, S	38	2	0	3	4	1	4	3	2	3	5	2	9	2.314
1838	MATH	141	Calculus/Differentiation	ERLBAUM, S	35	1	5	2	1	0	3	0	1	3	1	6	11	2.213
1839	MATH	141	Calculus/Differentiation	ERLBAUM, S	34	0	2	0	1	5	3	3	4	1	5	5	5	1.931
1840	MATH	141	Calculus/Differentiation	ADRIAN, M	37	1	2	1	1	4	3	0	2	0	2	8	9	1.879
1841	MATH	141	Calculus/Differentiation	BROGES, A	20	0	2	0	1	2	2	1	1	0	1	5	4	1.867
1842	MATH	141	Calculus/Differentiation	RALESCU, S	35	2	0	2	0	0	2	0	4	3	0	6	15	1.784
1843	MATH	141	Calculus/Differentiation	TOBAR, J	40	1	3	0	1	1	1	2	3	0	3	7	17	1.755
1844	MATH	141	Calculus/Differentiation	DON, E	35	0	2	0	2	2	0	0	3	0	4	6	13	1.611
1845	MATH	142	Calculus/Integration	MITRA, S	28	1	3	2	5	6	0	1	3	0	0	0	6	3.152
1846	MATH	142	Calculus/Integration	WEN, C	29	10	1	2	2	2	0	2	2	0	2	6	0	2.572
1847	MATH	142	Calculus/Integration	WEN, C	25	5	1	1	1	1	1	4	1	1	1	4	4	2.410
1848	MATH	142	Calculus/Integration	RONG, Y	21	2	1	1	0	0	2	3	4	0	3	1	4	2.294
1849	MATH	142	Calculus/Integration	RICCARDO, M	30	2	0	1	1	3	6	1	3	3	3	2	4	2.264
1850	MATH	142	Calculus/Integration	ISLAM, M	11	1	0	1	0	1	1	0	0	1	0	3	3	1.888
1851	MATH	142	Calculus/Integration	SULTAN, A	28	0	0	1	0	1	0	5	2	1	0	6	12	1.494
1852	MATH	142	Calculus/Integration	RONG, Y	22	0	0	0	0	1	1	3	1	1	5	3	7	1.420
1853	MATH	143	Calculus-Infinite Series	LIU, Z	22	3	1	2	0	1	3	1	1	2	0	1	7	2.813
1854	MATH	143	Calculus-Infinite Series	ERLBAUM, S	30	0	3	1	0	4	3	4	2	3	2	3	4	2.244
1855	MATH	143	Calculus-Infinite Series	ERLBAUM, S	30	2	1	1	3	1	4	1	3	3	3	4	4	2.146
1856	MATH	143	Calculus-Infinite Series	BRAUN, M	27	1	0	2	1	0	1	1	0	6	2	5	8	1.679
1857	MATH	143	Calculus-Infinite Series	BRAUN, M	28	2	1	0	1	0	1	1	0	1	1	9	11	1.353
1858	MATH	151	Calc/Diff & Integtn	PANDAZIS, M	25	2	0	0	1	1	0	0	5	0	5	0	9	2.093
1859	MATH	151	Calc/Diff & Integtn	DHARMA, J	27	1	1	1	0	0	2	1	3	1	0	3	12	2.085
1860	MATH	151	Calc/Diff & Integtn	SPITZ, H	29	3	0	2	3	1	0	1	1	1	4	5	8	2.014
1861	MATH	151	Calc/Diff & Integtn	DON, E	31	2	0	1	2	0	1	0	4	0	4	4	10	1.833
1862	MATH	151	Calc/Diff & Integtn	KAHAN, S	30	0	0	0	1	1	0	1	1	0	4	3	18	1.327
1863	MATH	152	Calc/Integration & Infinite	TERILLA, J	25	4	5	2	3	1	0	0	2	0	1	3	4	2.919
1864	MATH	152	Calc/Integration & Infinite	BERMAN, G	30	1	5	1	0	0	0	1	0	1	1	3	16	2.515
1865	MATH	152	Calc/Integration & Infinite	KOROVESHI, B	26	0	1	2	0	1	1	3	3	0	0	7	8	1.667
1866	MATH	152	Calc/Integration & Infinite	PORCHETTA, E	20	2	0	0	0	0	2	0	2	2	0	5	7	1.600
1867	MATH	152	Calc/Integration & Infinite	KAHAN, S	28	0	1	0	1	0	0	1	1	0	1	8	15	0.969
1868	MATH	201	Multivariable Calculus	ZAKERI, S	30	3	3	4	3	5	1	1	2	1	0	0	7	3.235
1869	MATH	201	Multivariable Calculus	SARIC, D	18	0	4	0	1	0	1	2	0	4	0	0	6	2.783
1870	MATH	202	Advanced Calculus	SARIC, D	25	3	7	2	1	0	3	3	1	1	0	0	4	3.305
1871	MATH	202	Advanced Calculus	ROTHENBERG, R	13	0	4	3	0	1	0	0	0	0	2	0	3	3.210
1872	MATH	209	Elementary Set Theory	KLOSIN, K	14	0	2	1	0	0	1	4	0	0	3	1	2	2.217
1873	MATH	220	Discrete Mathematics	GANGARAM, E	21	1	5	0	4	4	1	1	1	0	1	0	3	3.178
1874	MATH	220	Discrete Mathematics	MILLER, D	16	2	2	3	1	0	0	0	0	0	1	1	6	3.140
1875	MATH	223	Diff Equa/Num Meth 1	JOSEPH, M	20	0	4	4	0	1	4	0	2	2	1	1	1	2.789
1876	MATH	223	Diff Equa/Num Meth 1	BRAUN, M	28	1	2	3	3	4	4	2	1	3	1	2	2	2.635
1877	MATH	231	Linear Algebra I	SABITOVA, M	19	1	3	0	1	0	0	1	1	0	0	0	12	3.371
1878	MATH	231	Linear Algebra I	JIANG, Y	30	2	4	4	2	2	0	1	2	0	1	1	11	3.089
1879	MATH	231	Linear Algebra I	ZAKERI, S	30	4	5	1	1	4	3	2	4	2	1	1	2	2.861
1880	MATH	231	Linear Algebra I	JOSEPH, M	30	1	3	1	2	4	3	1	1	3	0	5	6	2.325
1881	MATH	231	Linear Algebra I	PANDAZIS, M	30	1	3	0	1	1	1	1	3	1	3	2	13	2.235
1882	MATH	231	Linear Algebra I	KOROVESHI, B	26	1	3	2	1	3	2	0	1	2	0	6	5	2.214
1883	MATH	231	Linear Algebra I	DHARMA, J	27	3	1	0	1	1	0	0	1	0	0	4	16	2.209
1884	MATH	231	Linear Algebra I	BERMAN, G	21	0	2	0	2	0	2	0	1	0	0	3	11	2.200
1885	MATH	231	Linear Algebra I	MILLER, D	24	0	1	2	1	3	1	1	1	2	2	3	7	2.124
1886	MATH	231	Linear Algebra I	KOROVESHI, B	24	0	0	1	0	0	1	3	3	5	0	2	9	1.853
1887	MATH	231	Linear Algebra I	VLAMIS, N	23	1	0	0	2	0	2	1	3	2	0	6	5	1.629
1888	MATH	241	Intro Prob & Math Stat	MITRA, S	25	0	3	2	4	2	4	1	0	0	0	0	8	3.231
1889	MATH	241	Intro Prob & Math Stat	MILLER, D	25	0	4	1	2	1	0	1	2	1	1	0	12	2.946
1890	MATH	241	Intro Prob & Math Stat	LIU, Z	35	2	3	2	4	2	4	3	1	2	0	1	11	2.904
1891	MATH	241	Intro Prob & Math Stat	SISSER, F	36	1	2	1	5	4	6	0	2	2	0	4	9	2.511
1892	MATH	241	Intro Prob & Math Stat	SISSER, F	36	3	2	4	2	0	6	0	4	7	0	3	4	2.500
1893	MATH	241	Intro Prob & Math Stat	GANGARAM, E	28	3	2	1	2	2	3	0	2	2	2	3	6	2.445
1894	MATH	241	Intro Prob & Math Stat	MILLER, D	25	1	2	2	1	0	1	1	1	0	2	3	11	2.264
1895	MATH	241	Intro Prob & Math Stat	NEVAREZ, B	36	2	3	3	0	1	1	4	4	4	4	4	6	2.160
1896	MATH	241	Intro Prob & Math Stat	GANGARAM, E	36	0	1	0	0	3	1	2	3	2	1	10	13	1.335
1897	MATH	242	Methods Of Math Statistics	SISSER, F	36	7	2	1	0	0	4	0	3	4	0	8	7	2.183
1898	MATH	247	Lin Prog & Game Thy	KLOSIN, K	36	4	16	0	1	1	4	2	2	0	0	2	4	3.303
1899	MATH	301	Abstract Algebra I	MILLER, R	15	0	0	0	0	0	0	0	0	1	0	1	13	0.850
1900	MATH	310	Elementary Real Analysis	WILSON, S	24	0	5	3	0	4	2	2	2	1	0	0	5	3.095
1901	MATH	505	Mathematical Problem-Solving	GANGARAM, E	30	1	2	3	2	4	5	1	5	3	0	4	0	2.420
1902	MATH	509	Set Theory and Logic	KLOSIN, K	19	0	3	0	0	2	3	2	1	0	0	5	3	2.044
1903	MATH	518	College Geometry	MARKINSON, M	21	2	5	1	5	1	1	1	1	0	0	3	1	2.910
1904	MATH	614	Functions of Real Variables	MITRA, S	11	2	3	2	2	1	1	0	0	0	0	0	0	3.609
1905	MATH	621	Probability	RALESCU, S	24	0	3	2	1	3	1	0	1	1	0	2	10	2.721
1906	MATH	624	Numerical Analysis I.	OVCHINNIKOV, A	15	3	3	2	0	1	0	1	1	0	0	1	3	3.225
1907	MATH	634	Theory of Graphs	HANUSA, C	12	5	4	0	0	0	0	0	0	1	0	1	1	3.427
1908	MATH	2902	Studies in Mathematics	ZOMBORY, B	10	1	3	2	0	1	0	0	0	0	0	0	2	3.771
1909	MATH	114W	Elementary Prob&Stat	CAI, A	29	11	6	2	0	7	0	0	0	0	0	0	3	3.708
1910	MATH	114W	Elementary Prob&Stat	RICCARDO, M	30	8	6	3	1	1	1	2	0	1	3	0	4	3.285

	SUBJECT	NBR	COURSE NAME	PROF	TOTAL	A+	A	A-	B+	B	B-	C+	C	C-	D	F	W	AVG GPA
1757	MATH	110	Math Literacy	ALI, M	37	7	9	3	4	2	2	1	1	0	1	0	5	3.500
1758	MATH	110	Math Literacy	ALI, M	37	10	7	2	1	3	3	3	1	0	1	0	5	3.410
1759	MATH	110	Math Literacy	LASKER, M	31	3	8	2	3	0	4	0	1	0	0	2	7	3.222
1760	MATH	110	Math Literacy	BEGA, J	37	4	0	5	3	3	3	4	2	2	0	1	6	2.893
1761	MATH	110	Math Literacy	WOLF-SONKIN, V	30	0	1	1	0	6	1	0	2	2	4	5	7	1.809
1762	MATH	114	Elementary Prob&Stat	LEHMAN, S	20	1	1	4	0	5	0	2	0	1	3	0	3	2.771
1763	MATH	115	College Algebra for Precalc	ALI, M	29	6	5	1	4	3	2	0	1	2	1	0	3	3.268
1764	MATH	115	College Algebra for Precalc	UDDIN, J	30	0	3	1	3	1	2	0	1	3	0	1	14	2.740
1765	MATH	115	College Algebra for Precalc	CLARKE, A	22	1	0	1	1	2	1	0	1	0	0	1	11	2.713
1766	MATH	115	College Algebra for Precalc	TOBAR, J	30	1	2	3	1	4	0	1	5	2	3	0	2	2.595
1767	MATH	115	College Algebra for Precalc	NAKAYAMA, A	38	4	2	0	2	3	3	2	4	1	1	3	8	2.520
1768	MATH	115	College Algebra for Precalc	NAKAYAMA, A	39	3	1	3	7	0	2	4	2	1	5	3	6	2.435
1769	MATH	115	College Algebra for Precalc	CLARKE, A	21	0	0	1	1	0	5	0	1	1	0	1	6	2.420
1770	MATH	115	College Algebra for Precalc	OSTROWSKY, A	37	3	4	1	2	1	0	0	2	2	6	2	12	2.378
1771	MATH	115	College Algebra for Precalc	BOTEJU, W	38	0	3	2	0	2	3	2	2	4	2	5	8	2.036
1772	MATH	115	College Algebra for Precalc	LEE, D	35	0	3	0	4	2	0	1	0	6	9	1	9	2.027
1773	MATH	115	College Algebra for Precalc	GILLMAN, P	30	3	0	0	0	2	0	2	0	1	3	5	9	1.706
1774	MATH	115	College Algebra for Precalc	CLARKE, A	22	0	0	0	1	1	0	1	1	2	0	3	7	1.556
1775	MATH	115	College Algebra for Precalc	WOLF-SONKIN, V	30	0	0	0	1	0	3	1	1	2	1	4	16	1.546
1776	MATH	115	College Algebra for Precalc	LEE, D	35	0	0	0	2	0	2	0	1	8	7	3	9	1.504
1777	MATH	115	College Algebra for Precalc	LEHMAN, S	21	1	1	0	0	0	2	0	2	0	1	6	5	1.415
1778	MATH	115	College Algebra for Precalc	GILLMAN, P	36	0	2	0	0	1	0	2	1	2	2	8	12	1.278
1779	MATH	115	College Algebra for Precalc	NEVAREZ, B	30	0	0	0	2	0	2	2	1	1	3	8	9	1.226
1780	MATH	115	College Algebra for Precalc	CHOW, J	29	0	0	0	2	0	0	1	3	0	5	8	4	1.047
1781	MATH	115	College Algebra for Precalc	GONZALEZ, R	14	0	0	1	0	0	0	0	2	1	1	7	1	0.867
1782	MATH	119	Math for Elem School Teachers	LEE, H	25	4	2	3	3	3	4	2	0	0	1	0	3	3.200
1783	MATH	119	Math for Elem School Teachers	GREENMAN, J	30	3	5	2	1	4	0	2	8	1	0	0	4	2.962
1784	MATH	119	Math for Elem School Teachers	CHOW, J	29	5	5	1	3	1	2	2	2	2	0	2	1	2.960
1785	MATH	119	Math for Elem School Teachers	LIN, Y	30	6	4	1	5	1	2	0	3	1	3	2	2	2.832
1786	MATH	119	Math for Elem School Teachers	LEHMAN, S	16	2	1	2	1	1	1	0	1	0	3	1	2	2.569
1787	MATH	119	Math for Elem School Teachers	BERGER, K	36	2	4	5	3	1	2	0	1	2	9	1	5	2.507
1788	MATH	119	Math for Elem School Teachers	SHEN, T	36	1	2	1	2	2	4	3	2	4	5	2	7	2.207
1789	MATH	120	Discrete Math Comp Sci	GOLDMAN, S	35	0	9	6	2	3	2	9	0	1	2	0	1	3.047
1790	MATH	120	Discrete Math Comp Sci	WANG, A	35	4	2	1	2	0	3	5	2	3	2	1	10	2.600
1791	MATH	120	Discrete Math Comp Sci	JOANIDHI, Z	30	0	8	1	0	1	0	1	5	0	0	6	6	2.318
1792	MATH	120	Discrete Math Comp Sci	BERGER, K	35	0	1	0	2	6	3	2	2	2	4	2	11	2.196
1793	MATH	120	Discrete Math Comp Sci	BOTEJU, W	36	2	5	1	1	1	1	5	3	4	2	6	5	2.161
1794	MATH	120	Discrete Math Comp Sci	HANUSA, C	30	2	2	2	1	1	2	3	0	1	2	6	7	2.077
1795	MATH	120	Discrete Math Comp Sci	BOTEJU, W	35	1	1	2	2	4	2	0	2	1	5	6	8	1.927
1796	MATH	120	Discrete Math Comp Sci	BERGER, K	36	0	1	0	1	3	2	1	5	2	4	6	10	1.656
1797	MATH	120	Discrete Math Comp Sci	ADRIAN, M	37	0	2	0	1	1	3	0	4	3	7	8	8	1.466
1798	MATH	122	Precalculus	CAI, A	23	1	1	5	0	0	2	1	1	0	0	0	0	3.291
1799	MATH	122	Precalculus	CLEARY, J	38	6	11	3	1	2	0	1	2	1	5	0	4	3.169
1800	MATH	122	Precalculus	DHARMA, J	30	1	0	1	0	0	3	0	0	0	0	0	16	3.160
1801	MATH	122	Precalculus	LEE, H	35	5	1	2	4	3	4	2	0	3	0	0	11	3.088
1802	MATH	122	Precalculus	ARCHETTI, M	37	10	3	1	2	6	0	1	3	2	1	2	5	3.000
1803	MATH	122	Precalculus	CLEARY, J	35	3	3	4	2	2	6	2	3	0	4	0	4	2.834
1804	MATH	122	Precalculus	KALRA, P	20	3	1	0	0	0	4	3	2	0	0	1	6	2.693
1805	MATH	122	Precalculus	ISLAM, M	20	1	0	4	1	2	0	1	2	0	0	2	4	2.646
1806	MATH	122	Precalculus	OZKAN, E	10	0	3	0	0	0	0	3	0	0	2	0	2	2.613
1807	MATH	122	Precalculus	BROGES, A	21	0	4	1	0	6	1	1	1	0	1	3	1	2.539
1808	MATH	122	Precalculus	SPITZ, H	35	1	4	3	2	2	2	0	0	0	5	3	11	2.459
1809	MATH	122	Precalculus	LI, H	37	5	2	1	1	1	4	2	0	1	2	5	11	2.379
1810	MATH	122	Precalculus	LORD, A	37	0	4	0	4	1	1	2	4	0	4	2	9	2.341
1811	MATH	122	Precalculus	MARKOWITZ, E	40	0	3	4	3	4	3	1	3	2	4	4	8	2.339
1812	MATH	122	Precalculus	FLYNN, D	35	1	3	3	2	4	2	1	3	5	6	3	2	2.239
1813	MATH	122	Precalculus	COLON, C	37	2	1	0	0	1	0	2	1	3	4	0	19	2.193
1814	MATH	122	Precalculus	YUABOV, D	37	3	2	2	0	0	3	3	1	3	2	5	12	2.146
1815	MATH	122	Precalculus	MARKOWITZ, E	37	1	3	0	4	3	1	0	3	3	7	3	4	2.107
1816	MATH	122	Precalculus	JOSEPH, M	35	0	1	0	0	3	3	2	2	5	1	3	12	1.960
1817	MATH	122	Precalculus	SHEN, T	30	0	0	0	2	1	2	3	2	2	5	2	7	1.805
1818	MATH	122	Precalculus	JOSEPH, M	37	0	1	1	1	3	2	1	2	4	3	5	13	1.804
1819	MATH	122	Precalculus	GANT, S	35	0	2	2	0	1	0	0	2	1	4	4	14	1.756
1820	MATH	122	Precalculus	SPITZ, H	35	1	2	1	2	5	0	3	1	1	4	10	5	1.730
1821	MATH	122	Precalculus	TOLEDO, D	37	0	0	0	0	0	2	2	4	1	0	7	15	1.231
1822	MATH	122	Precalculus	TOLEDO, D	37	0	0	0	0	0	1	0	1	2	6	11	10	0.671
1823	MATH	131	Calculus with Applications I	GJONLEKAJ, M	16	2	2	2	0	2	0	2	1	0	0	1	4	3.000
1824	MATH	131	Calculus with Applications I	GREENMAN, J	29	1	7	0	0	2	1	1	4	1	3	0	9	2.785
1825	MATH	131	Calculus with Applications I	SABITOVA, M	20	0	2	0	1	4	0	1	4	2	0	1	5	2.467
1826	MATH	131	Calculus with Applications I	GREENMAN, J	24	1	1	0	1	2	2	0	4	2	2	3	5	2.006
1827	MATH	131	Calculus with Applications I	GREENMAN, J	31	0	4	0	2	3	1	3	2	0	3	7	6	1.928
1828	MATH	131	Calculus with Applications I	JOANIDHI, Z	25	0	2	0	1	3	1	1	4	0	0	6	6	1.850
1829	MATH	132	Calc Appl Soc Sci II	GOLDMAN, S	12	1	0	1	2	2	1	1	0	0	1	0	2	2.922
1830	MATH	141	Calculus/Differentiation	BROGES, A	19	5	3	1	0	1	1	0	0	0	1	1	3	3.262
1831	MATH	141	Calculus/Differentiation	KALRA, P	20	2	3	3	0	0	1	2	2	0	0	1	5	3.029
1832	MATH	141	Calculus/Differentiation	FLYNN, D	35	2	6	3	4	2	3	3	2	2	1	1	5	2.955
1833	MATH	141	Calculus/Differentiation	GOLDMAN, S	30	1	4	3	4	3	1	2	3	3	2	2	2	2.632
1834	MATH	141	Calculus/Differentiation	GONZALEZ, R	35	3	1	2	6	2	1	4	2	2	0	4	6	2.537
1835	MATH	141	Calculus/Differentiation	KIMATOV, M	38	7	3	2	0	2	2	2	1	3	4	5	5	2.403
1836	MATH	141	Calculus/Differentiation	WANG, A	34	3	1	1	5	2	4	2	2	0	2	5	7	2.356
1837	MATH	141	Calculus/Differentiation	NICASTRO, S	38	2	0	3	4	1	4	3	2	3	5	2	9	2.314
1838	MATH	141	Calculus/Differentiation	ERLBAUM, S	35	1	5	2	1	0	3	0	1	3	1	6	11	2.213
1839	MATH	141	Calculus/Differentiation	ERLBAUM, S	34	0	2	0	1	5	3	3	4	1	5	5	5	1.931
1840	MATH	141	Calculus/Differentiation	ADRIAN, M	37	1	2	1	1	4	3	0	2	0	2	8	9	1.879
1841	MATH	141	Calculus/Differentiation	BROGES, A	20	0	2	0	1	2	2	1	1	0	1	5	4	1.867
1842	MATH	141	Calculus/Differentiation	RALESCU, S	35	2	0	2	0	0	2	0	4	3	0	6	15	1.784
1843	MATH	141	Calculus/Differentiation	TOBAR, J	40	1	3	0	1	1	1	2	3	0	3	7	17	1.755
1844	MATH	141	Calculus/Differentiation	DON, E	35	0	2	0	2	2	0	0	3	0	4	6	13	1.611
1845	MATH	142	Calculus/Integration	MITRA, S	28	1	3	2	5	6	0	1	3	0	0	0	6	3.152
1846	MATH	142	Calculus/Integration	WEN, C	29	10	1	2	2	2	0	2	2	0	2	6	0	2.572
1847	MATH	142	Calculus/Integration	WEN, C	25	5	1	1	1	1	1	4	1	1	1	4	4	2.410
1848	MATH	142	Calculus/Integration	RONG, Y	21	2	1	1	0	0	2	3	4	0	3	1	4	2.294
1849	MATH	142	Calculus/Integration	RICCARDO, M	30	2	0	1	1	3	6	1	3	3	3	2	4	2.264
1850	MATH	142	Calculus/Integration	ISLAM, M	11	1	0	1	0	1	1	0	0	1	0	3	3	1.888
1851	MATH	142	Calculus/Integration	SULTAN, A	28	0	0	1	0	1	0	5	2	1	0	6	12	1.494
1852	MATH	142	Calculus/Integration	RONG, Y	22	0	0	0	0	1	1	3	1	1	5	3	7	1.420
1853	MATH	143	Calculus-Infinite Series	LIU, Z	22	3	1	2	0	1	3	1	1	2	0	1	7	2.813
1854	MATH	143	Calculus-Infinite Series	ERLBAUM, S	30	0	3	1	0	4	3	4	2	3	2	3	4	2.244
1855	MATH	143	Calculus-Infinite Series	ERLBAUM, S	30	2	1	1	3	1	4	1	3	3	3	4	4	2.146
1856	MATH	143	Calculus-Infinite Series	BRAUN, M	27	1	0	2	1	0	1	1	0	6	2	5	8	1.679
1857	MATH	143	Calculus-Infinite Series	BRAUN, M	28	2	1	0	1	0	1	1	0	1	1	9	11	1.353
1858	MATH	151	Calc/Diff & Integtn	PANDAZIS, M	25	2	0	0	1	1	0	0	5	0	5	0	9	2.093
1859	MATH	151	Calc/Diff & Integtn	DHARMA, J	27	1	1	1	0	0	2	1	3	1	0	3	12	2.085
1860	MATH	151	Calc/Diff & Integtn	SPITZ, H	29	3	0	2	3	1	0	1	1	1	4	5	8	2.014
1861	MATH	151	Calc/Diff & Integtn	DON, E	31	2	0	1	2	0	1	0	4	0	4	4	10	1.833
1862	MATH	151	Calc/Diff & Integtn	KAHAN, S	30	0	0	0	1	1	0	1	1	0	4	3	18	1.327
1863	MATH	152	Calc/Integration & Infinite	TERILLA, J	25	4	5	2	3	1	0	0	2	0	1	3	4	2.919
1864	MATH	152	Calc/Integration & Infinite	BERMAN, G	30	1	5	1	0	0	0	1	0	1	1	3	16	2.515
1865	MATH	152	Calc/Integration & Infinite	KOROVESHI, B	26	0	1	2	0	1	1	3	3	0	0	7	8	1.667
1866	MATH	152	Calc/Integration & Infinite	PORCHETTA, E	20	2	0	0	0	0	2	0	2	2	0	5	7	1.600
1867	MATH	152	Calc/Integration & Infinite	KAHAN, S	28	0	1	0	1	0	0	1	1	0	1	8	15	0.969
1868	MATH	201	Multivariable Calculus	ZAKERI, S	30	3	3	4	3	5	1	1	2	1	0	0	7	3.235
1869	MATH	201	Multivariable Calculus	SARIC, D	18	0	4	0	1	0	1	2	0	4	0	0	6	2.783
1870	MATH	202	Advanced Calculus	SARIC, D	25	3	7	2	1	0	3	3	1	1	0	0	4	3.305
1871	MATH	202	Advanced Calculus	ROTHENBERG, R	13	0	4	3	0	1	0	0	0	0	2	0	3	3.210
1872	MATH	209	Elementary Set Theory	KLOSIN, K	14	0	2	1	0	0	1	4	0	0	3	1	2	2.217
1873	MATH	220	Discrete Mathematics	GANGARAM, E	21	1	5	0	4	4	1	1	1	0	1	0	3	3.178
1874	MATH	220	Discrete Mathematics	MILLER, D	16	2	2	3	1	0	0	0	0	0	1	1	6	3.140
1875	MATH	223	Diff Equa/Num Meth 1	JOSEPH, M	20	0	4	4	0	1	4	0	2	2	1	1	1	2.789
1876	MATH	223	Diff Equa/Num Meth 1	BRAUN, M	28	1	2	3	3	4	4	2	1	3	1	2	2	2.635
1877	MATH	231	Linear Algebra I	SABITOVA, M	19	1	3	0	1	0	0	1	1	0	0	0	12	3.371
1878	MATH	231	Linear Algebra I	JIANG, Y	30	2	4	4	2	2	0	1	2	0	1	1	11	3.089
1879	MATH	231	Linear Algebra I	ZAKERI, S	30	4	5	1	1	4	3	2	4	2	1	1	2	2.861
1880	MATH	231	Linear Algebra I	JOSEPH, M	30	1	3	1	2	4	3	1	1	3	0	5	6	2.325
1881	MATH	231	Linear Algebra I	PANDAZIS, M	30	1	3	0	1	1	1	1	3	1	3	2	13	2.235
1882	MATH	231	Linear Algebra I	KOROVESHI, B	26	1	3	2	1	3	2	0	1	2	0	6	5	2.214
1883	MATH	231	Linear Algebra I	DHARMA, J	27	3	1	0	1	1	0	0	1	0	0	4	16	2.209
1884	MATH	231	Linear Algebra I	BERMAN, G	21	0	2	0	2	0	2	0	1	0	0	3	11	2.200
1885	MATH	231	Linear Algebra I	MILLER, D	24	0	1	2	1	3	1	1	1	2	2	3	7	2.124
1886	MATH	231	Linear Algebra I	KOROVESHI, B	24	0	0	1	0	0	1	3	3	5	0	2	9	1.853
1887	MATH	231	Linear Algebra I	VLAMIS, N	23	1	0	0	2	0	2	1	3	2	0	6	5	1.629
1888	MATH	241	Intro Prob & Math Stat	MITRA, S	25	0	3	2	4	2	4	1	0	0	0	0	8	3.231
1889	MATH	241	Intro Prob & Math Stat	MILLER, D	25	0	4	1	2	1	0	1	2	1	1	0	12	2.946
1890	MATH	241	Intro Prob & Math Stat	LIU, Z	35	2	3	2	4	2	4	3	1	2	0	1	11	2.904
1891	MATH	241	Intro Prob & Math Stat	SISSER, F	36	1	2	1	5	4	6	0	2	2	0	4	9	2.511
1892	MATH	241	Intro Prob & Math Stat	SISSER, F	36	3	2	4	2	0	6	0	4	7	0	3	4	2.500
1893	MATH	241	Intro Prob & Math Stat	GANGARAM, E	28	3	2	1	2	2	3	0	2	2	2	3	6	2.445
1894	MATH	241	Intro Prob & Math Stat	MILLER, D	25	1	2	2	1	0	1	1	1	0	2	3	11	2.264
1895	MATH	241	Intro Prob & Math Stat	NEVAREZ, B	36	2	3	3	0	1	1	4	4	4	4	4	6	2.160
1896	MATH	241	Intro Prob & Math Stat	GANGARAM, E	36	0	1	0	0	3	1	2	3	2	1	10	13	1.335
1897	MATH	242	Methods Of Math Statistics	SISSER, F	36	7	2	1	0	0	4	0	3	4	0	8	7	2.183
1898	MATH	247	Lin Prog & Game Thy	KLOSIN, K	36	4	16	0	1	1	4	2	2	0	0	2	4	3.303
1899	MATH	301	Abstract Algebra I	MILLER, R	15	0	0	0	0	0	0	0	0	1	0	1	13	0.850
1900	MATH	310	Elementary Real Analysis	WILSON, S	24	0	5	3	0	4	2	2	2	1	0	0	5	3.095
1909	MATH	114W	Elementary Prob&Stat	CAI, A	29	11	6	2	0	7	0	0	0	0	0	0	3	3.708
1910	MATH	114W	Elementary Prob&Stat	RICCARDO, M	30	8	6	3	1	1	1	2	0	1	3	0	4	3.285

	CLASS NUMBER	PROF	AVG GPA PROF	AVG GPA PROF LETTER	STD DEV GPA PROF
0	110	ALI, M	3.5	B+	0.77
1	110	LASKER, M	3.2	B	1.15
2	110	BEGA, J	2.9	B-	0.92
3	110	WOLF-SONKIN, V	1.8	C-	1.29
4	110	All Professors	3.0	B	1.13
5	114	LEHMAN, S	2.8	B-	1.02
6	115	ALI, M	3.3	B+	0.86
7	115	UDDIN, J	2.7	B-	1.10
8	115	CLARKE, A	2.2	C	1.22
9	115	TOBAR, J	2.6	C+	1.00
10	115	NAKAYAMA, A	2.5	C+	1.25
11	115	OSTROWSKY, A	2.4	C+	1.42
12	115	BOTEJU, W	2.0	C	1.32
13	115	LEE, D	1.8	C-	1.07
14	115	GILLMAN, P	1.5	D	1.45
15	115	WOLF-SONKIN, V	1.5	D	1.17
16	115	LEHMAN, S	1.4	D	1.51
17	115	NEVAREZ, B	1.2	D	1.22
18	115	CHOW, J	1.0	D	1.11
19	115	GONZALEZ, R	0.9	F	1.17
20	115	All Professors	2.0	C	1.36
21	119	LEE, H	3.2	B	0.74
22	119	GREENMAN, J	3.0	B	0.87
23	119	CHOW, J	3.0	B	1.19
24	119	LIN, Y	2.8	B-	1.27
25	119	LEHMAN, S	2.6	C+	1.35
26	119	BERGER, K	2.5	C+	1.30
27	119	SHEN, T	2.2	C	1.11
28	119	All Professors	2.7	B-	1.18
29	120	GOLDMAN, S	3.0	B	0.88
30	120	WANG, A	2.6	C+	1.09
31	120	JOANIDHI, Z	2.3	C+	1.62
32	120	BERGER, K	1.9	C-	1.15
33	120	BOTEJU, W	2.0	C	1.40
34	120	HANUSA, C	2.1	C	1.53
35	120	ADRIAN, M	1.5	D	1.22
36	120	All Professors	2.2	C	1.35
37	122	CAI, A	3.3	B+	0.69
38	122	CLEARY, J	3.0	B	1.08
39	122	DHARMA, J	3.2	B	0.57
40	122	LEE, H	3.1	B	0.75
41	122	ARCHETTI, M	3.0	B	1.17
42	122	KALRA, P	2.7	B-	1.05
43	122	ISLAM, M	2.6	C+	1.30
44	122	OZKAN, E	2.6	C+	1.19
45	122	BROGES, A	2.5	C+	1.36
46	122	SPITZ, H	2.1	C	1.52
47	122	LI, H	2.4	C+	1.50
48	122	LORD, A	2.3	C+	1.24
49	122	MARKOWITZ, E	2.2	C	1.28
50	122	FLYNN, D	2.2	C	1.23
51	122	COLON, C	2.2	C	1.10
52	122	YUABOV, D	2.1	C	1.43
53	122	JOSEPH, M	1.9	C-	1.16
54	122	SHEN, T	1.8	C-	0.98
55	122	GANT, S	1.8	C-	1.46
56	122	TOLEDO, D	1.0	D	0.99
57	122	All Professors	2.3	C+	1.35
58	131	GJONLEKAJ, M	3.0	B	1.15
59	131	GREENMAN, J	2.2	C	1.35
60	131	SABITOVA, M	2.5	C+	0.99
61	131	JOANIDHI, Z	1.8	C-	1.43
62	131	All Professors	2.3	C+	1.34
63	132	GOLDMAN, S	2.9	B-	0.83
64	141	BROGES, A	2.6	C+	1.56
65	141	KALRA, P	3.0	B	1.15
66	141	FLYNN, D	3.0	B	1.02
67	141	GOLDMAN, S	2.6	C+	1.17
68	141	GONZALEZ, R	2.5	C+	1.26
69	141	KIMATOV, M	2.4	C+	1.48
70	141	WANG, A	2.4	C+	1.36
71	141	NICASTRO, S	2.3	C+	1.13
72	141	ERLBAUM, S	2.1	C	1.38
73	141	ADRIAN, M	1.9	C-	1.52
74	141	RALESCU, S	1.8	C-	1.42
75	141	TOBAR, J	1.8	C-	1.49
76	141	DON, E	1.6	D	1.42
77	141	All Professors	2.3	C+	1.40
78	142	MITRA, S	3.2	B	0.63
79	142	WEN, C	2.5	C+	1.52
80	142	RONG, Y	1.9	C-	1.14
81	142	RICCARDO, M	2.3	C+	1.06
82	142	ISLAM, M	1.9	C-	1.60
83	142	SULTAN, A	1.5	D	1.23
84	142	All Professors	2.2	C	1.34
85	143	LIU, Z	2.8	B-	1.11
86	143	ERLBAUM, S	2.2	C	1.22
87	143	BRAUN, M	1.5	D	1.47
88	143	All Professors	2.0	C	1.37
89	151	PANDAZIS, M	2.1	C	1.05
90	151	DHARMA, J	2.1	C	1.36
91	151	SPITZ, H	2.0	C	1.51
92	151	DON, E	1.8	C-	1.38
93	151	KAHAN, S	1.3	D	1.12
94	151	All Professors	1.9	C-	1.35
95	152	TERILLA, J	2.9	B-	1.43
96	152	BERMAN, G	2.5	C+	1.68
97	152	KOROVESHI, B	1.7	C-	1.44
98	152	PORCHETTA, E	1.6	D	1.44
99	152	KAHAN, S	1.0	D	1.39
100	152	All Professors	1.9	C-	1.63
101	201	ZAKERI, S	3.2	B	0.70
102	201	SARIC, D	2.8	B-	0.97
103	201	All Professors	3.0	B	0.83
104	202	SARIC, D	3.3	B+	0.80
105	202	ROTHENBERG, R	3.2	B	1.14
106	202	All Professors	3.3	B+	0.93
107	209	KLOSIN, K	2.2	C	1.23
108	220	GANGARAM, E	3.2	B	0.79
109	220	MILLER, D	3.1	B	1.36
110	220	All Professors	3.2	B	1.03
111	223	JOSEPH, M	2.8	B-	1.12
112	223	BRAUN, M	2.6	C+	1.09
113	223	All Professors	2.7	B-	1.10
114	231	SABITOVA, M	3.4	B+	0.81
115	231	JIANG, Y	3.1	B	1.11
116	231	ZAKERI, S	2.9	B-	1.05
117	231	JOSEPH, M	2.3	C+	1.38
118	231	PANDAZIS, M	2.2	C	1.32
119	231	KOROVESHI, B	2.0	C	1.33
120	231	DHARMA, J	2.2	C	1.76
121	231	BERMAN, G	2.2	C	1.55
122	231	MILLER, D	2.1	C	1.31
123	231	VLAMIS, N	1.6	D	1.33
124	231	All Professors	2.4	C+	1.38
125	241	MITRA, S	3.2	B	0.52
126	241	MILLER, D	2.6	C+	1.35
127	241	LIU, Z	2.9	B-	0.94
128	241	SISSER, F	2.5	C+	1.19
129	241	GANGARAM, E	1.9	C-	1.43
130	241	NEVAREZ, B	2.2	C	1.29
131	241	All Professors	2.5	C+	1.29
132	242	SISSER, F	2.2	C	1.58
133	247	KLOSIN, K	3.3	B+	1.10
134	301	MILLER, R	0.8	F	0.85
135	310	WILSON, S	3.1	B	0.77
136	114W	CAI, A	3.7	A-	0.44
137	114W	RICCARDO, M	3.3	B+	1.04
138	114W	All Professors	3.5	B+	0.83

Data Analysis Presented by:¶

Average GPA for the entire subject of MATH in Fall 2022: 2.3, which is a C+, with the hardest subject being MATH 151 with a 1.9¶

Average Withdrawal Rate for any individual class for the entire subject of MATH in Fall 2022 is 24.98% ¶

Nonetheless, if the distribution of scores is approximately normal (i.e., follows a bell curve), the empirical rule states:¶

Bringing it all together:¶

IMPORTANT FUNCTIONS USED IN THIS NOTEBOOK:¶

Note: These will be used for doing the rest of the analysis of subjects by class!¶

SCROLL DOWN FOR DATA VISUALIZATIONS AND INDIVIDUAL STATS ON TEACHERS!¶

Also, check this website out to get some basics on formating text with Jupyter notebook!¶

The isinstance() function returns True if the specified object is of the specified type, otherwise False (https://stackoverflow.com/questions/1549801/what-are-the-differences-between-type-and-isinstance)¶

dropna():¶

pd.set_option :¶

pd.reset_option¶

In pandas, both unique and nunique are used to get unique values of a series object, but they serve different purposes and return different types of output:¶

iloc[]:¶

sort_values():¶

extend():¶

np.std():¶

Note¶

To actually interact with plot below and be able to hover over plots and see which teacher its referring to, download the html version of this file instead¶

Teacher Analysis¶

Most of the teachers above make sense based off their Rate My Professor:¶

Other's don't:¶

Couldn't find any info on this professor:¶

Teachers that missed the mark because of gpa, withdrawal rate, and/or only teaching one class, but they have a rate my professor of 4 and above:¶

The hardest class based on average GPA in Fall 2022 is class number: 151 with an average GPA of 1.9, which is a C- (tied with 152 after further analysis) ¶

	PROF	AVG GPA PROF	AVG GPA PROF LETTER	STD DEV GPA PROF	NUM OF CLASSES	WITHDRAW PERCENTAGE
0	ADRIAN, M	1.7	C-	1.4	2	23.0
1	ALI, M	3.4	B+	0.8	3	12.6
2	ARCHETTI, M	3.0	B	1.2	1	13.5
3	BEGA, J	2.9	B-	0.9	1	16.2
4	BERGER, K	2.1	C	1.2	3	24.3
5	BERMAN, G	2.4	C+	1.6	2	52.9
6	BOTEJU, W	2.0	C	1.4	3	19.3
7	BRAUN, M	1.9	C-	1.4	3	25.3
8	BROGES, A	2.6	C+	1.5	3	13.3
9	CAI, A	3.5	B+	0.6	2	5.8
10	CHOW, J	2.0	C	1.5	2	8.6
11	CLARKE, A	2.2	C	1.2	3	36.9
12	CLEARY, J	3.0	B	1.1	2	11.0
13	COLON, C	2.2	C	1.1	1	51.4
14	DHARMA, J	2.5	C+	1.5	3	52.4
15	DON, E	1.7	C-	1.4	2	34.8
16	ERLBAUM, S	2.1	C	1.3	4	18.6
17	FLYNN, D	2.6	C+	1.2	2	10.0
18	GANGARAM, E	2.3	C+	1.4	3	25.9
19	GANT, S	1.8	C-	1.5	1	40.0
20	GILLMAN, P	1.5	D	1.4	2	31.8
21	GJONLEKAJ, M	3.0	B	1.2	1	25.0
22	GOLDMAN, S	2.9	B-	1.0	3	6.5
23	GONZALEZ, R	1.7	C-	1.5	2	14.3
24	GREENMAN, J	2.4	C+	1.3	4	21.1
25	HANUSA, C	2.1	C	1.5	1	23.3
26	ISLAM, M	2.3	C+	1.5	2	22.6
27	JIANG, Y	3.1	B	1.1	1	36.7
28	JOANIDHI, Z	2.1	C	1.6	2	21.8
29	JOSEPH, M	2.2	C	1.3	4	26.2
30	KAHAN, S	1.1	D	1.3	2	56.9
31	KALRA, P	2.9	B-	1.1	2	27.5
32	KIMATOV, M	2.4	C+	1.5	1	13.2
33	KLOSIN, K	2.8	B-	1.2	2	12.0
34	KOROVESHI, B	1.9	C-	1.4	3	28.9
35	LASKER, M	3.2	B	1.2	1	22.6
36	LEE, D	1.8	C-	1.1	2	25.7
37	LEE, H	3.1	B	0.8	2	23.3
38	LEHMAN, S	2.3	C+	1.4	3	17.5
39	LI, H	2.4	C+	1.5	1	29.7
40	LIN, Y	2.8	B-	1.3	1	6.7
41	LIU, Z	2.9	B-	1.0	2	31.6
42	LORD, A	2.3	C+	1.2	1	24.3
43	MARKOWITZ, E	2.2	C	1.3	2	15.6
44	MILLER, D	2.6	C+	1.4	4	40.0
45	MILLER, R	0.8	F	0.8	1	86.7
46	MITRA, S	3.2	B	0.6	2	26.4
47	NAKAYAMA, A	2.5	C+	1.3	2	18.2
48	NEVAREZ, B	1.7	C-	1.3	2	22.7
49	NICASTRO, S	2.3	C+	1.1	1	23.7
50	OSTROWSKY, A	2.4	C+	1.4	1	32.4
51	OZKAN, E	2.6	C+	1.2	1	20.0
52	PANDAZIS, M	2.2	C	1.2	2	40.0
53	PORCHETTA, E	1.6	D	1.4	1	35.0
54	RALESCU, S	1.8	C-	1.4	1	42.9
55	RICCARDO, M	2.8	B-	1.2	2	13.3
56	RONG, Y	1.9	C-	1.1	2	25.6
57	ROTHENBERG, R	3.2	B	1.1	1	23.1
58	SABITOVA, M	2.9	B-	1.0	2	43.6
59	SARIC, D	3.0	B	0.9	2	23.3
60	SHEN, T	2.0	C	1.1	2	21.2
61	SISSER, F	2.4	C+	1.3	3	18.5
62	SPITZ, H	2.1	C	1.5	3	24.2
63	SULTAN, A	1.5	D	1.2	1	42.9
64	TERILLA, J	2.9	B-	1.4	1	16.0
65	TOBAR, J	2.2	C	1.3	2	27.1
66	TOLEDO, D	1.0	D	1.0	2	33.8
67	UDDIN, J	2.7	B-	1.1	1	46.7
68	VLAMIS, N	1.6	D	1.3	1	21.7
69	WANG, A	2.5	C+	1.2	2	24.6
70	WEN, C	2.5	C+	1.5	2	7.4
71	WILSON, S	3.1	B	0.8	1	20.8
72	WOLF-SONKIN, V	1.7	C-	1.3	2	38.3
73	YUABOV, D	2.1	C	1.4	1	32.4
74	ZAKERI, S	3.0	B	0.9	2	15.0